Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks

• Main Authors: Croom, Brendan P, Berkson, Michael, Mueller, Robert K, Presley, Michael, Storck, Steven
• Format: Journal Article
• Language: English
• Published: 21.05.2021
• Subjects: Physics - Materials Science
• DOI: 10.48550/arxiv.2105.10564

In context of the universal presence of defects in additively manufactured (AM) metals, efficient computational tools are required to rapidly screen AM microstructures for mechanical integrity. To this end, a deep learning approach is used to predict the elastic stress fields in images of defect-containing metal microstructures. A large dataset consisting of the stress response of 100,000 random microstructure images is generated using high-resolution Fast Fourier Transform-based finite element (FFT-FE) calculations, which is then used to train a modified U-Net style convolutional neural network (CNN) model. The trained U-Net model more accurately predicted the stress response compared to alternative CNN architectures, exceeded the accuracy of low-resolution FFT-FE calculations, and was generalizable to microstructures with complex defect geometries. The model was applied to images of real AM microstructures with severe lack of fusion defects, and predicted a strong linear increase of maximum stress as a function of pore fraction. Together, the proposed CNN offers an efficient and accurate way to predict the structural response of defect-containing AM microstructures.